Mm wave radar for enhanced mobility applications

ABSTRACT

Disclosed herein are systems, devices, and processes that use millimeter wave radar or other remote sensing to enhance mobility applications. Obstacles may be detected using remote sensing. Acceleration of a mobility apparatus may be controlled based on detection of the obstacle. The controlling may be performed based on characteristics of the obstacle, including location, type of obstacle, and/or trajectory of the obstacle.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. application Ser.No. 16/657,771, filed Oct. 18, 2019, which is incorporated herein in itsentirety by this reference thereto.

TECHNICAL FIELD

This patent document relates to systems, devices, and processes that usemillimeter wave radar or other remote sensing to enhance mobilityapplications.

BACKGROUND

The existence of mobile apparatuses, such as electric scooters, electricbicycles, and electric skateboards are known. These mobility apparatusesmay be powered by a motor. A user of the mobility apparatus may controlacceleration and steering of the mobile apparatus.

SUMMARY

Disclosed herein are systems, devices, and processes that use millimeterwave radar or other remote sensing to enhance mobility applications.Obstacles may be detected using remote sensing. Acceleration of a mobileapparatus may be controlled based on detection of the obstacle. Thecontrolling may be performed based on characteristics of the obstacle,including location, type of obstacle, and/or trajectory of the obstacle.

A system is disclosed. The system includes a mobility apparatus. Thesystem includes a remote sensor configured to generate sensor data abouta vicinity of the mobility apparatus. The system includes a computingmodule configured to determine, based on the sensor data, whether anobstacle is present in the vicinity of the mobility apparatus. Thesystem includes a drive controller configured to alter the accelerationof the mobility apparatus responsive to the determination by thecomputing module as to whether an obstacle is present in the vicinity ofthe mobility apparatus.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a mobility system according to someembodiments of the present disclosure.

FIG. 2 is a block diagram of a mobility system according to someembodiments of the present disclosure.

FIG. 3 is a block diagram of a remote sensor according to someembodiments of the present disclosure.

FIG. 4 is a block diagram of a computing module according to someembodiments of the present disclosure.

FIG. 5 is a schematic diagram of remote sensing for a mobility apparatusaccording to some embodiments of the present disclosure.

FIG. 6 is a schematic diagram of remote sensing for a mobility apparatusaccording to some embodiments of the present disclosure.

FIG. 7 is a flowchart for a process of controlling a mobility apparatusaccording to some embodiments of the present disclosure.

FIG. 8 is a flowchart for a process of controlling a mobility apparatusaccording to some embodiments of the present disclosure.

FIG. 9A is a schematic diagram of remote sensing for a mobilityapparatus according to some embodiments of the present disclosure.

FIG. 9B is a schematic diagram of remote sensing for a mobilityapparatus according to some embodiments of the present disclosure.

FIG. 9C is a schematic diagram of remote sensing for a mobilityapparatus according to some embodiments of the present disclosure.

FIG. 9D is a schematic diagram of remote sensing for a mobilityapparatus according to some embodiments of the present disclosure.

FIG. 9E is a schematic diagram of remote sensing for a mobilityapparatus according to some embodiments of the present disclosure.

FIG. 10 is a flowchart for a process of controlling a mobility apparatusaccording to some embodiments of the present disclosure.

FIG. 11 is a flowchart for a process of controlling a mobility apparatusaccording to some embodiments of the present disclosure.

FIG. 12 is a flowchart for a process of controlling a mobility apparatusaccording to some embodiments of the present disclosure.

FIG. 13A is a schematic diagram of remote sensing for a mobilityapparatus according to some embodiments of the present disclosure.

FIG. 13B is a schematic diagram of remote sensing for a mobilityapparatus according to some embodiments of the present disclosure.

FIG. 14 is a flowchart for a process of controlling a mobility apparatusaccording to some embodiments of the present disclosure.

FIG. 15 is a flowchart for a process of controlling a mobility apparatusaccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The use of mobility apparatuses, such as electric scooters, electricbicycles, and electric skateboards, has increased significantly inrecent years. This has been spurred particularly by the expansion of themarket for so called “dockless” scooters and “dockless” bicycles. Withthese systems, electric-powered mobility apparatuses may be left oversignificant portions of an urban area for human users to “rent” or“check out.” A user that rents a mobility apparatus, e.g., a docklessscooter, may be able to use the scooter for several hours or severalminutes, with the fee increasing with the proportion of time that theuser uses the scooter.

These system have become particular popular recently, for variousreasons. First, users are typically able to find a dockless scooteranywhere in a given urban area without travelling very far. Second, theuser is often able to rent the scooter for a rather small fee. Third,because the dockless scooter is electric-powered, the user is able totravel on the dockless scooter without having to exert any significantphysical energy. For these and other reasons, dockless scooters andother mobility apparatuses have become a common choice for users'transportation over short distances. When combined with other,longer-distance transportation (e.g., a commuter train), the mobilityapparatuses are often used to fill the “last mile mobility” role for theuser. For similar reasons, mobility apparatuses are sometimes referredto as providing “micro mobility.”

But the proliferation of mobility apparatuses have also created numerousproblems. First, because the mobility apparatuses may be “dockless,”they may be left in a wide variety of inappropriate locations (e.g.,blocking a sidewalk). Second, because many of the users only rent themobility apparatuses, and only do so for short periods of time, theaverage user of a mobility apparatus may be quite novice. Third, becausethere are numerous competing dockless systems, across which a user mayspread his time, the user may not ultimately be particularly familiarwith the equipment (e.g., the type of scooter, the accelerationcharacteristics of the scooter, the steering characteristics of thescooter) that the user is using at any given point in time. Fourth,there is often inconsistency with whether a user operates a mobilityapparatus in the street, in a protected lane adjacent to the street, onthe sidewalk, or elsewhere. This causes confusion for the users of themobility apparatuses as well as pedestrians, automobile drivers, andother people that interact with the mobility apparatus users in theurban environments.

Because of these problems, and others, a high rate of injuries, deaths,and other incidents have been reported with users of mobilityapparatuses. Hence a solution is needed for mobility apparatuses thatimproves the safety of the apparatuses in light of the foregoingproblems. But a user-implemented solution is unlikely to be feasible, ashuman preferences and market forces make it unlikely that any mobilityapparatus user or mobility apparatus system operator will seek trainingor some other approach to reducing the rate of incidents on mobilityapparatuses. Instead, a technological solution is needed that willaddress these problems.

FIG. 1 is a schematic diagram of a mobility system 100 according to someembodiments of the present disclosure.

The mobility system 100 may include a mobility apparatus 105. Themobility apparatus 105 may be provided as an electric scooter. Themobility apparatus 105 may be provided in other forms in variousembodiments, such as an electric bicycle, and electric skateboard, achildren's electric vehicle (e.g., power wheels), a segway, amotorcycle, or otherwise. In some embodiments, the mobility apparatus105 may be provided as a Xiaomi M365 scooter.

The mobility system 100 may include a drive controller 110. The drivercontroller 110 may be an electronic controller that controls variousphysical drive components of the mobility apparatus 105, such asregenerative brakes, non-regenerative brakes, a throttle, or others. Thedrive controller 110 may control the acceleration of the mobilityapparatus 105 by, e.g., activating the regenerative brakes, deactivatingthe throttle, or otherwise.

The mobility system 100 may include a remote sensor 120. The remotesensor 120 may be able to sense the vicinity the mobility apparatus 105.For example, the remote sensor 120 may be able to detect obstacles inthe area around the mobility apparatus, such as light poles,pedestrians, dogs, automobiles, or others. In some embodiments, theremote sensor 120 may be provided as a millimeter wave radar module. Insuch embodiments, the millimeter wave radar module may use transmissionand receipt of radar waves in the millimeter range to detect obstaclesin the vicinity of the mobility apparatus 105. The remote sensor 120 maybe provided in other forms in various embodiments, such as a differentform of electromagnetic radar, an acoustic wave remote sensor, lidar, orotherwise. In some embodiments, the remote sensor 120 may be provided asan Alpine millimeter wave radar module.

In some embodiments, implementation of the remote sensor 120 as amillimeter wave radar module may be advantageous for various reasons.For example, a millimeter wave radar module may provide higherresolution sensing than other remote sensing technologies, such as asingle point ultrasonic sensor. As another example, a millimeter waveradar module may allow for more flexible implementations as compared toother remote sensing technologies. For instance, the millimeter waveradar module may not require a line-of-sight type implementation. Asanother example, a millimeter wave radar module may allow the remotesensor 120 to be implemented with a more discrete physical profile. Forinstance, the millimeter wave radar module may not require a visibleaperture, an exposed mirror, or other less discreet features of otherremote sensing technologies. As another example, a millimeter wave radarmodule may allow for a simpler and lower cost implementation. Forinstance, the millimeter wave radar module may not require the greatercomplexity and higher cost associated with implementing moving parts asrequired by other remote sensing technologies (e.g., lidar). As anotherexample, a millimeter wave radar module may have lower powerrequirements than other remote sensing technologies (e.g., lidar,camera). This may be especially important in a mobility system, wherethe source of power may come from a battery with a limited duration ofcharge, and for which higher costs are required for recharging (e.g.,team of workers to retrieve, charge, and return mobility systemnightly).

The mobility system 100 may include a computing module 130. Thecomputing module 130 may operate to process the sensor data from theremote sensor 120 in order to determine whether an obstacle is presentin the vicinity of the mobility apparatus 105. The computing module 130may determine other information about such an obstacle, such as itslocation, its size, its trajectory of motion, the time of object thatthe obstacle is, or others. The computing module 130 may providecommands to the drive controller 110 in response to thesedeterminations. As such, the computing module 130 may control operationof the mobility apparatus 105 based on the detection of an obstacle inthe vicinity of the mobility apparatus 105. The computing module 130 maybe provided in a variety of forms, such as a system on a chip, a minicomputer, a electronic controller, a processor in some other component(e.g., as a component of the drive controller 110 or the remote sensor120), or otherwise. In some embodiments, the computing module 130 may beprovided as a Raspberry Pi.

FIG. 2 is a block diagram of a mobility system 100 according to someembodiments of the present disclosure. The mobility system 100 mayinclude components as described previously, including a drive controller110, a remote sensor 120, and a computing module 130.

The mobility system 100 may also include a motor 250 and/or brakes 260.The mobility system 100 may use the motor 250 and/or brakes 260 tocontrol the acceleration (including both increase and decrease invelocity) of the mobility apparatus 105. In some embodiments, the motor250 may be an electric motor. The motor 250 maybe provided in a varietyof other forms in various embodiments, such as a gasoline engine, adiesel engine, manually-cranked engine, or others. The brakes 260 may beprovided as regenerative brakes. In such embodiments, the brakes 260 maybe provided integrated with the motor 250, such as for allowing themotor to recapture energy generated by the activation of the brakes 260.The brakes 260 may be provided in other forms in various embodiments,such as non-regenerative braking, disk brakes, drum brakes, or others.

FIG. 3 is a block diagram of a remote sensor 120 according to someembodiments of the present disclosure. The remote sensor 120 may beprovided as described elsewhere herein.

The remote sensor 120 may include a wave transmitter 310. The wavetransmitter 310 may operate to transmit an electromagnetic way into thevicinity of the mobility apparatus 105. In some embodiments, the wavetransmitter 310 may transmit an electromagnetic wave in the millimeterrange. The wave transmitter 310 may transmit other types ofelectromagnetic or other waves in various embodiments.

The remote sensor 120 may include an energy sensor 320. The energysensor 320 may detect the presence of reflected wave energy in thevicinity of the mobility apparatus 105. For example, the energy sensor320 may detect energy from a wave previously transmitted by the wavetransmitter 310 that has reflected off of an obstacle in the vicinity ofthe mobility apparatus 105 and then returned in the direction of themobility apparatus 105. In some embodiments, the energy sensor 310 maybe a millimeter wave detector. The energy sensor 320 may detect othertypes of electromagnetic energy or other energy in various embodiments.

The remote sensor 120 may include an image processor 330. The imageprocessor 330 may process information about energy detected by theenergy sensor 320 in order to generate image data representative of thevicinity of the mobility apparatus 105. For example, the image processor330 may process data from energy sensor 320 in order to generate atwo-dimensional field of data that represents the magnitude of energydetected in the area in front of the mobility apparatus 105. Forexample, the image processor 330 may generate an image with high datavalues in a direction where data from the energy sensor 320 indicates ashort period of energy reflection (e.g., wave energy reflected off of anobject and returned to the energy sensor 320 in a very short period oftime). The image processor 330 may be provided in other forms in variousembodiments.

The remote sensor 120 may include additional components, such as memory340, transceiver 350, and/or power input 360. The remote sensor 120 mayuse the memory 340 in order to store data processed by and/or generatedby the image processor 330. For example, the image processor 330 maystored the X recent images generated based on processing data from theenergy sensor 320. X may be two in some embodiments. The memory 340 maystore other data in various embodiments. The remote sensor 120 may usetransceiver 350 to transmit and/or receive data. For example the remotesensor 120 may use the transceiver 350 to transmit image data processedby and/or generated by the image processor 330 to other components inthe mobility system 100 (e.g., computing module 130). The remote sensormay use transceiver 350 to receive configuration parameters from othercomponents of the mobility system 100, (e.g., from computing module130). The transceiver 350 may send and/or receive other data in variousembodiments. The remote sensor 120 may use power input 360 to provideelectric energy to other components of the remote sensor 120. Powerinput 360 may be provided in various forms, such as battery, a directcurrent input line, an alternating current input line, an alternatingcurrent input line with rectifier, or others.

FIG. 4 is a block diagram of a computing module 130 according to someembodiments of the present disclosure. The computing module 130 may beprovided as described elsewhere herein.

The computing module 130 may include a processor 420. The computingmodule 130 may use the processor 420 to process data received fromremote sensor 120. For example the processor 420 may process image datareceived from remote sensor 120 (e.g., as processed by and/or generatedby image processor 330). The processor 420 may process other sensor datagenerated by the remote sensor 120.

The processor 420 may process the sensor data in order to determinewhether an obstacle is present in the vicinity of the mobility apparatus105. For example, the processor 420 may process sensor data from theremote sensor 120 to determine whether there is a large spatial area inthe vicinity of the mobility apparatus 105 for which transmitted waveenergy was reflected back towards the mobility apparatus 105 in arelatively short period of time. The processor 420 may process sensordata (e.g., a two-dimensional field generated by image processor 330) inorder to detect such an obstacle. The processor may determine whether anobstacle is present in the vicinity of the mobility apparatus 105 inother ways in various embodiments.

The processor 420 may use stored data to determine whether an obstacleis present in the vicinity of the mobility apparatus 105. For example,the processor 420 may use parameters stored in memory 430. Theparameters may include, for example, an energy threshold value thatidentifies a minimum amount of energy necessary for the sensor data tobe interpreted as identifying an obstacle. As another example, theprocessor 420 may use artificial intelligence parameters stored inmemory 430. The processor 420 may retrieve parameters defining anartificial neural network, which the processor 420 may use to determinewhether an obstacle is present in the vicinity of the mobility apparatus105. The processor 420 may use other stored data in various embodiments.

The processor 420 may generate control instructions in variousembodiments. The processor 420 may generate control instructions basedon determining that an obstacle is present in the vicinity of themobility apparatus 105. For example, if the processor 420 determinesthat an obstacle is present in the vicinity of the mobility apparatus105, the processor 420 may generate a reduce velocity (i.e.,“decelerate”) instruction. The processor 420 may transmit the controlinstruction to the drive controller 110, using the transceiver 410. Theprocessor 420 may generate other control instruction in variousembodiments, such as an increase velocity instruction, a brakeinstruction, an increase braking instruction, a decrease brakinginstruction, a disengage throttle instruction, an increase throttleinstruction, a decrease throttle instruction, a steering instruction, asteer left instruction, a steer right instruction, and/or others.

The processor 420 may filter sensor data in various embodiments. Theprocessor 420 may receive sensor data generated by the remote sensor120. In such embodiments, the processor 420 may filter the receivedsensor data. For example, the processor 420 may discard extraneous data.As another example, the process 420 may perform a transform operation onthe sensor data in order to process the sensor data in a differentdomain (e.g., in the frequency domain). The processor 420 may filter thesensor data in other ways in various embodiments. In some embodiment, adifferent component may filter sensor data, such as image processor 330or another component of remote sensor 120.

The processor 420 may filter output data in various embodiments. Theprocessor 420 may filter results of applying sensor data to a neuralnetwork structure. For example, the processor 420 may filter amongdifferent types of identified objects in the environment around amobility system. The processor 420 may filter stationary objects frommobile objects. The processor 420 may filter non-obstacle objects fromobstacle objects. The processor 420 may filter objects based on aclassification of the objects (e.g., person, vehicle, stationaryobstacle). The processor 420 may filter the output data in other ways invarious embodiments. In some embodiments the processor 420 may generatecontrol instructions based on the filtering of the output data.

The computing module 130 may include additional components, such asmemory 430, transceiver 410, and/or power input 440. The computingmodule 130 may use the memory 430 as described previously. The memory430 may store other data in various embodiments. The computing module130 may use transceiver 410 as described previously. The transceiver 410may transmit and/or receive data. For example the computing module 130may use the transceiver 410 to receive image data from other componentsin the mobility system 100 (e.g., remote sensor 120). The computingmodule 130 may use transceiver 410 to transmit data to other componentsin the mobility system 100 (e.g., drive controller 110). The transceiver410 may send and/or receive other data in various embodiments. Thecomputing module 130 may use power input 440 to provide electric energyto other components of the computing module 130. Power input 440 may beprovided in various forms, such as battery, a direct current input line,an alternating current input line, an alternating current input linewith rectifier, or others.

FIG. 5 is a schematic diagram of remote sensing 520 for a mobilityapparatus according to some embodiments of the present disclosure. Theremote sensing 520 may be performed by remote sensor 120. The remotesensor 120 may be attached to the mobility apparatus 105. For example,the remote sensor 120 may be attached to the handlebars and/or verticalpost at the front of the mobility apparatus 105. The remote sensor 120may be provided in a forward-facing position. With such a configuration,the remote sensor 120 may be capable of sensing the area in front of themobility apparatus 105.

The remote sensing 520 may include sensing one or more sectors. Forexample, the remote sensing 520 may include sensing sector 531, sector532, sector 533, sector 534, sector 535, sector 541, sector 542, sector543, sector 544, sector 545, sector 551, sector 552, sector 553, sector554, sector 555, sector 561, sector 562, sector 563, sector 564, and/orsector 565. For instance, remote sensing 520 may involve the remotesensor 120 transmitting radar waves into the space in front of themobility apparatus 105, as illustrated in overhead view in FIG. 5. Theremote sensing 520 may be capable of detecting the presence of obstaclespresent in any of the sectors 531 to 565 using the remote sensor 120.

FIG. 6 is a schematic diagram of remote sensing 520 for a mobilityapparatus according to some embodiments of the present disclosure.Remote sensing 520 may be provided as described elsewhere herein.

As illustrated, remote sensing 520 may include a classification orcategorization of one or more of the sectors 531 to 565. For instance,the remote sensing 520 may identify a “low risk” category for sector531, sector 535, sector 541, sector 545, sector 551, sector 555, sector561, and sector 565. The remote sensing 520 may identify a “high risk”category for sector 532, sector 533, sector 534, sector 542, sector 543,sector 544, sector 552, sector 553, sector 554, sector 562, sector 563,and sector 564. Remote sensing 520 may identify a sector as “high risk”if there is a higher risk that an obstacle present in that sector willcause a collision with the mobility apparatus 105. Remote sensing 520may identify a sector as “low risk” if there is a lower risk that anobstacle present in that sector will cause a collision with the mobilityapparatus 105. Remote sensing 520 may use other categories or classesfor sectors in various embodiments.

FIG. 7 is a flowchart for a process 700 of controlling a mobilityapparatus according to some embodiments of the present disclosure. Theprocess 700 may be performed using the mobility system 100 in someembodiments.

At block 710, remote sensing is performed in the area around a mobilityapparatus. The remote sensing may include using a remote sensor 120, asdescribed elsewhere herein. The remote sensing may include sensing anarea in front of the mobility apparatus 105 using millimeter wave radar.

At block 720, sensor data is analyzed. The data analysis may includeusing a computing module 130 to process sensor data captured by theremote sensor 120, as described elsewhere herein. In some embodiments,the data analysis may be performed by other components, such as imageprocessor 330, as described elsewhere herein.

The data analysis of block 720 may include analyzing sensor data todetermine whether an obstacle is present in the area around the mobilityapparatus. For example, the data analysis may include determiningwhether an obstacle is present in one or more sectors sensed by theremote sensor 120 in front of the mobility apparatus 105. The dataanalysis of block 720 may include applying a decision tree structure orother previously-trained structure.

At block 730, a determination is made as to whether there is anobstacle. For example, a determination may be made as to whether anobstacle is present in one or more sectors sensed by the remote sensor120 in front of the mobility apparatus 105. In some embodiments, block730 may include using a result of the analysis performed at block 720.

At block 730, if it is determined that no obstacle is present, then theprocess continues at block 710.

At block 730, if it is determined that an obstacle is present, then theprocess continues at block 740.

At block 740, acceleration of a mobility apparatus is controlled. Theacceleration of the mobility apparatus 105 may be controlled using thedrive controller 110, as described elsewhere herein. The acceleration ofthe mobility apparatus may be controlled based on a control instructiongenerated based on the analysis of the sensor data at block 720 and/oras a result of the determination at block 730. In some embodiments,controlling the acceleration of the mobility apparatus may includeapplying brakes of the mobility apparatus, releasing brakes of themobility apparatus, engaging throttle of the mobility apparatus, and/ordisengaging a throttle of the mobility apparatus.

In various embodiments, the process 700 may include more or fewer blocksthan those just escribed. For example, the process 700 may include theremote sensor 120 transmitting sensor data to the computing device 130.As another example, the process 700 may include the computing devicefiltering the sensor data received from the remote sensor 120. In suchembodiments, the computing module 130 may perform the data analysis ofblock 720 using the filtered sensor data. As another example, theprocess 700 may include the computing device filtering an output of theprocessing at block 720.

FIG. 8 is a flowchart for a process 800 of controlling a mobilityapparatus according to some embodiments of the present disclosure. Theprocess 800 may be performed using the mobility system 100 in someembodiments.

At block 810, remote sensing is performed in the area around a mobilityapparatus. The remote sensing may include using a remote sensor 120, asdescribed elsewhere herein. The remote sensing may include sensing anarea in front of the mobility apparatus 105 using millimeter wave radar.

At block 820, sensor data is analyzed. The data analysis may includeusing a computing module 130 to process sensor data captured by theremote sensor 120, as described elsewhere herein. In some embodiments,the data analysis may be performed by other components, such as imageprocessor 330, as described elsewhere herein.

The data analysis of block 820 may include analyzing sensor data todetermine whether an obstacle is present in the area around the mobilityapparatus. For example, the data analysis may include determiningwhether an obstacle is present in one or more sectors sensed by theremote sensor 120 in front of the mobility apparatus 105. The dataanalysis of block 820 may include applying a decision tree structure orother previously-trained structure.

The data analysis of block 820 may include analyzing sensor data todetermine whether an obstacle is present in a high risk sector in thearea around the mobility apparatus. For example, the data analysis mayinclude determining whether an obstacle is present in one or moresectors sensed by the remote sensor 120 in front of the mobilityapparatus 105 that are categorized as corresponding to a high risk ofcollision.

At block 830, a determination is made as to whether there is an obstaclein a high risk sector. For example, a determination may be made as towhether an obstacle is present in one or more sectors sensed by theremote sensor 120 in front of the mobility apparatus 105 that arecategorized as corresponding to a high risk of collision. In someembodiments, block 830 may include using a result of the analysisperformed at block 820.

At block 830, if it is determined that no obstacle is present in a highrisk sector, then the process continues at block 810.

At block 830, if it is determined that an obstacle is present, then theprocess continues at block 840.

At block 840, acceleration of a mobility apparatus is controlled. Theacceleration of the mobility apparatus 105 may be controlled using thedrive controller 110, as described elsewhere herein. The acceleration ofthe mobility apparatus may be controlled based on a control instructiongenerated based on the analysis of the sensor data at block 820 and/oras a result of the determination at block 830. In some embodiments,controlling the acceleration of the mobility apparatus may includeapplying brakes of the mobility apparatus, releasing brakes of themobility apparatus, engaging throttle of the mobility apparatus, and/ordisengaging a throttle of the mobility apparatus.

In various embodiments, the process 800 may include more or fewer blocksthan those just escribed. For example, the process 800 may include theremote sensor 120 transmitting sensor data to the computing device 130.As another example, the process 800 may include the computing devicefiltering the sensor data received from the remote sensor 120. In suchembodiments, the computing module 130 may perform the data analysis ofblock 820 using the filtered sensor data. As another example, theprocess 800 may include the computing device filtering an output of theprocessing at block 820.

FIG. 9A is a schematic diagram of remote sensing 920 for a mobilityapparatus according to some embodiments of the present disclosure.Remote sensing 920 may be provided as described elsewhere herein.

As illustrated, remote sensing 920 may include a classification orcategorization of one or more of the sectors. The classification orcategorization may be provided as disclosed elsewhere herein. Forinstance, the remote sensing 920 may identify a “low risk” category forsome sectors, and a “high risk” category for other sectors, as disclosedelsewhere herein. Remote sensing 920 may use other categories or classesfor sectors in various embodiments.

As illustrated, remote sensing 920 may apply classifications orcategories to sectors differently than disclosed elsewhere herein. Forinstance, remote sensing 920 may categorize as “high risk” sectors thatare close to, and centered in front of, the mobility apparatus 105. Inthis embodiment, remote sensing 920 may not categorize sector 562,sector 563, or sector 564 as “high risk.” For example, even thoughsectors 562, 563, and 564 are centered in front of the mobilityapparatus 105, sectors 562, 563, and 564 are not as close to themobility apparatus 105, relative to other sectors. Remote sensing 920may apply these or other categorizations as the result of an artificialintelligence algorithm, such as by training a classification treestructure using decision parameters of centrality and distance. Remotesensing 920 may use a multi-parameter decision tree structure based ondifferent parameters in some embodiments. Remote sensing 920 may use adifferent multi-parameter structure in some embodiments. Remote sensing920 may use an artificial neural network in some embodiments.

FIG. 9B is a schematic diagram of remote sensing 930 for a mobilityapparatus according to some embodiments of the present disclosure.Remote sensing 930 may be provided as described elsewhere herein.

As illustrated, remote sensing 930 may include a classification orcategorization of one or more of the sectors. The classification orcategorization may be provided as disclosed elsewhere herein. Forinstance, the remote sensing 930 may categorize sectors based on levelof risk that the mobility apparatus 105 will collide with an obstacle inthat sector.

As illustrated, remote sensing 930 may apply more than two classes orcategories to the sectors. For example, remote sensing 930 may apply a“high risk” category (illustrated with cross-hatching) to some sectors,a “medium risk” category (illustrated in hatching) to some sectors, anda “low risk” category (illustrated without hatching) to some sectors.The remote sensing 930 may apply the more than two classes or categoriesto indicate a gradient of risk that an obstacle in the various sectorswill result in a collision with the mobility apparatus 105.

Remote sensing 930 may apply these or other categorizations as theresult of an artificial intelligence algorithm, such as by training aclassification tree structure using decision parameters of centralityand distance. Remote sensing 930 may use a multi-parameter decision treestructure based on different parameters in some embodiments. Remotesensing 930 may use a different multi-parameter structure in someembodiments. Remote sensing 930 may use an artificial neural network insome embodiments.

FIG. 9C is a schematic diagram of remote sensing 940 for a mobilityapparatus according to some embodiments of the present disclosure.Remote sensing 940 may be provided as described elsewhere herein.

As illustrated, remote sensing 940 may include a classification orcategorization of one or more of the sectors. The classification orcategorization may be provided as disclosed elsewhere herein. Forinstance, the remote sensing 940 may identify a “low risk” category forsome sectors, and a “high risk” category for other sectors, as disclosedelsewhere herein. Remote sensing 940 may use other categories or classesfor sectors in various embodiments.

As illustrated, remote sensing 940 may apply classifications orcategories to sectors differently than disclosed elsewhere herein. Forinstance, remote sensing 940 may categorize as “high risk” sectors thatare close to, and centered in front of, the mobility apparatus 105.Remote sensing 940 may categorize more sectors based on sensor data fora larger area in front of the mobility apparatus 105. Remote sensing 920may apply these or other categorizations as the result of an artificialintelligence algorithm, such as by training a classification treestructure using decision parameters of centrality and distance. Remotesensing 920 may use a multi-parameter decision tree structure based ondifferent parameters in some embodiments. Remote sensing 920 may use adifferent multi-parameter structure in some embodiments. Remote sensing920 may use an artificial neural network in some embodiments.

FIG. 9D is a schematic diagram of remote sensing 950 for a mobilityapparatus according to some embodiments of the present disclosure.Remote sensing 950 may be provided as described elsewhere herein.

As illustrated, remote sensing 950 may include a classification orcategorization of one or more of the sectors. The classification orcategorization may be provided as disclosed elsewhere herein. Forinstance, the remote sensing 950 may identify a “low risk” category forsome sectors, and a “high risk” category for other sectors, as disclosedelsewhere herein. Remote sensing 950 may use other categories or classesfor sectors in various embodiments.

As illustrated, remote sensing 950 may apply classifications orcategories to sectors differently than disclosed elsewhere herein. Forinstance, remote sensing 950 may categorize as “high risk” sectors in away that is not symmetric. For example, remote sensing 950 maycategorize as “high risk” sectors without requiring symmetry about thecenter axis of the mobility device 110. Remote sensing 950 may applythese or other categorizations as the result of an artificialintelligence algorithm, such as by training an artificial neural networkbased on training data reflecting actual obstacle measurements andcollision outcomes. The categorization resulting from the application ofthe artificial intelligence algorithm may reflect real-world conditionsthat may not be evident by human observation. For example, theillustration of FIG. 9D may reflect that users of mobility apparatuslike mobility apparatus 105 are less likely to notice an obstacle to theuser's left and more likely to notice an obstacle to the user's right.Remote sensing 950 may use a different artificial intelligence algorithmin some embodiments.

FIG. 9E is a schematic diagram of remote sensing 960 for a mobilityapparatus according to some embodiments of the present disclosure.Remote sensing 960 may be provided as described elsewhere herein.

As illustrated, remote sensing 960 may include more granular sectors(i.e., more sectors per unit of area or volume) than disclosed elsewhereherein. In some embodiments, remote sensing 960 may determine thegranularity of the sectors based on the resolution of the remote sensor120. This determination may be made by, for example, computing module130. In some embodiments, remote sensing 960 may determine thegranularity of the sectors dynamically, while the mobility apparatus isin operation. This determination may be made by, for example, computingmodule 130. In some embodiments, remote sensing 960 may determine thegranularity of the sectors dynamically based on the environment in whichthe mobility apparatus 105 is present. For example, remote sensing 960may determine to use more granular sectors based on determining that themobility apparatus is in a confined environment, such as a sidewalk(e.g., by determine the frequency of obstacles in the range ofdetection). As an example, remote sensing 960 may determine to use lessgranular sectors based on determining that the mobility apparatus is inan open environment, such as a bike lane (e.g., by determine thefrequency of obstacles in the range of detection). This determinationmay be made by, for example, computing module 130.

The remote sensing 920, remote sensing 930, remote sensing 940, remotesensing 950, and remote sensing 960 may be using in combination withother aspects of the present disclosure. For example, the granularity ofsectors described with respect to remote sensing 960 may be combinedwith the asymmetrical categorization described with respect to remotesensing 950. As another example, the processes 700 and 800 may be usedin combination with remote sensing 920, remote sensing 930, remotesensing 940, remote sensing 950, and/or remote sensing 960.

FIG. 10 is a flowchart for a process 1000 of controlling a mobilityapparatus according to some embodiments of the present disclosure. Theprocess 1000 may be performed using the mobility system 100 in someembodiments.

At block 1010, remote sensing is performed in the area around a mobilityapparatus. The remote sensing may include using a remote sensor 120, asdescribed elsewhere herein. The remote sensing may include sensing anarea in front of the mobility apparatus 105 using millimeter wave radar.

At block 1020, sensor data is analyzed. The data analysis may includeusing a computing module 130 to process sensor data captured by theremote sensor 120, as described elsewhere herein. In some embodiments,the data analysis may be performed by other components, such as imageprocessor 330, as described elsewhere herein.

The data analysis of block 1020 may include analyzing sensor data todetermine whether an obstacle is present in the area around the mobilityapparatus. For example, the data analysis may include determiningwhether an obstacle is present in one or more sectors sensed by theremote sensor 120 in front of the mobility apparatus 105. The dataanalysis of block 1020 may include applying a decision tree structure orother previously-trained structure.

At block 1030, a determination is made as to whether there is anobstacle. For example, a determination may be made as to whether anobstacle is present in one or more sectors sensed by the remote sensor120 in front of the mobility apparatus 105. In some embodiments, block1030 may include using a result of the analysis performed at block 1020.

At block 1030, if it is determined that no obstacle is present, then theprocess continues at block 1010.

At block 1030, if it is determined that an obstacle is present, then theprocess continues at block 1040.

At block 1040, a determination is made as to the type of obstacle. Thedetermination may be made by computing module 130 in some embodiments.The determination may include classifying the detected obstacle into oneor more predetermined categories. For example, a determination may bemade as to whether the obstacle is a pedestrian, a telephone pole, anautomobile, or a bicycle. As another example, a determination may bemade as to whether the obstacle is a stationary obstacle or mobileobstacle. The determination of the type of obstacle may be performedusing the sensor data captured by the remote sensor 120. Thedetermination may be performed based on an artificial intelligencealgorithm, such as by using a neural network structure trained usingpast sensor data for known types of obstacles.

At block 1050, a determination is made as to whether the sector theobstacle is present in is a high risk sector based on the type ofobstacle. The determination may be made by computing module 130 in someembodiments. For example, the determination may include determiningthat, for an obstacle detected in sector 552 (with reference to FIG. 5),that that is not a high risk sector based on block 1040 resulting in adetermination that obstacle is a stationary obstacle (e.g., a telephonepole in sector 552 is not a high risk of collision, because it isunlikely to move). As another example, the determination may includedetermining that, for an obstacle detected in sector 552 (with referenceto FIG. 5), that that is a high risk sector based on block 1040resulting in a determination that obstacle is a mobile obstacle (e.g., apedestrian in sector 552 is a high risk of collision, because it islikely to move).

The determination at block 1050 may be performed in a variety of ways.The determination at block 1050 may be performed based on performedusing a neural network structure trained using past sensor data andactual collision outcomes. The determination may be performed using aset of predetermined rules, based on the parameters of obstacle type andobstacle location. The determination may be performed using a predefinedcontinuous value function, based on input parameters of obstacle typeand obstacle location. The determination may be performed using apredefined discrete value function, based on input parameters ofobstacle type and obstacle location. The determination at block 1050 maybe performed in other ways in various embodiments.

In some embodiments, the process 1000 may include an additional block ofdetermining the sector in which the obstacle is located. In otherembodiments, this determination may be performed as part of block 1020,1040, and/or 1050.

At block 1050, if it is determined that the obstacle is not in a highrisk sector based on the type of obstacle, then the process continues atblock 1010.

At block 1050, if it is determined that the obstacle is in a high risksector based on the type of obstacle, then the process continues atblock 1060.

At block 1060, acceleration of a mobility apparatus is controlled. Theacceleration of the mobility apparatus 105 may be controlled using thedrive controller 110, as described elsewhere herein. The acceleration ofthe mobility apparatus may be controlled based on a control instructiongenerated based on the analysis of the sensor data at block 1020 and/oras a result of the determination at block 1050. In some embodiments,controlling the acceleration of the mobility apparatus may includeapplying brakes of the mobility apparatus, releasing brakes of themobility apparatus, engaging throttle of the mobility apparatus, and/ordisengaging a throttle of the mobility apparatus.

In various embodiments, the process 1000 may include more or fewerblocks than those just escribed. For example, the process 1000 mayinclude the remote sensor 120 transmitting sensor data to the computingdevice 130. As another example, the process 1000 may include thecomputing device filtering the sensor data received from the remotesensor 120. In such embodiments, the computing module 130 may performthe data analysis of block 1020 using the filtered sensor data. Asanother example, the process 1000 may include the computing devicefiltering an output of the processing at block 1020.

FIG. 11 is a flowchart for a process 1100 of controlling a mobilityapparatus according to some embodiments of the present disclosure. Theprocess 1100 may be performed using the mobility system 100 in someembodiments.

At block 1110, remote sensing is performed in the area around a mobilityapparatus. The remote sensing may include using a remote sensor 120, asdescribed elsewhere herein. The remote sensing may include sensing anarea in front of the mobility apparatus 105 using millimeter wave radar.

At block 1120, sensor data is analyzed. The data analysis may includeusing a computing module 130 to process sensor data captured by theremote sensor 120, as described elsewhere herein. In some embodiments,the data analysis may be performed by other components, such as imageprocessor 330, as described elsewhere herein.

The data analysis of block 1120 may include analyzing sensor data todetermine whether an obstacle is present in the area around the mobilityapparatus. For example, the data analysis may include determiningwhether an obstacle is present in one or more sectors sensed by theremote sensor 120 in front of the mobility apparatus 105. The dataanalysis of block 1120 may include applying a decision tree structure orother previously-trained structure.

At block 1130, a determination is made as to whether there is anobstacle. For example, a determination may be made as to whether anobstacle is present in one or more sectors sensed by the remote sensor120 in front of the mobility apparatus 105. In some embodiments, block1130 may include using a result of the analysis performed at block 1120.

At block 1130, if it is determined that no obstacle is present, then theprocess continues at block 1110.

At block 1130, if it is determined that an obstacle is present, then theprocess continues at block 1140.

At block 1140, a quantity of acceleration control is calculated. Thecalculation may be made by computing module 130 in some embodiments. Thecalculation may be made by drive controller 110 in some embodiments.

The calculation at block 1140 may include calculating an amount ofvelocity change to apply to the mobility apparatus 105. For example,block 1140 may include calculating an amount of adjustment to a throttleof the mobility apparatus 105. As another example, block 1140 mayinclude calculating an amount of braking to apply using a brake of themobility apparatus 105.

In some embodiment, the calculation at block 1140 may be performed basedon one or more parameters. For example, the calculation may be performedbased on the sector in which the obstacle is present (e.g., more brakingfor an obstacle in sector 542 than for an obstacle in sector 562 (withreference to FIG. 5)). As another example, the calculation may beperformed based on the type of obstacle (e.g., more braking for a mobileobstacle than for a stationary obstacle). As another example, thecalculation may be performed based on a combination of the sector inwhich the obstacle is present and the type of obstacle (e.g., morebraking for a stationary obstacle in sector 533 than for a mobileobstacle in sector 552 (with reference to FIG. 5)). The calculation ofblock 1140 may be performed based on other parameters in variousembodiments. In some embodiments, the calculation of block 1140 may beperformed based on an artificial intelligence structure, such as adecision tree structure or an artificial neural network structure.

At block 1150, acceleration of a mobility apparatus is controlled. Theacceleration of the mobility apparatus 105 may be controlled using thedrive controller 110, as described elsewhere herein. The acceleration ofthe mobility apparatus may be performed based on the calculationperformed at block 1140. The acceleration of the mobility apparatus maybe controlled based on a control instruction generated based on thecalculation performed at block 1140. In some embodiments, controllingthe acceleration of the mobility apparatus may include applying brakesof the mobility apparatus, releasing brakes of the mobility apparatus,engaging throttle of the mobility apparatus, and/or disengaging athrottle of the mobility apparatus.

In various embodiments, the process 1100 may include more or fewerblocks than those just escribed. For example, the process 1100 mayinclude the remote sensor 120 transmitting sensor data to the computingdevice 130. As another example, the process 1100 may include thecomputing device filtering the sensor data received from the remotesensor 120. In such embodiments, the computing module 130 may performthe data analysis of block 1120 using the filtered sensor data. Asanother example, the process 1100 may include the computing devicefiltering an output of the processing at block 1120.

FIG. 12 is a flowchart for a process 1200 of controlling a mobilityapparatus according to some embodiments of the present disclosure. Theprocess 1200 may be performed using the mobility system 100 in someembodiments.

At block 1210, remote sensing is performed in the area around a mobilityapparatus. The remote sensing may include using a remote sensor 120, asdescribed elsewhere herein. The remote sensing may include sensing anarea in front of the mobility apparatus 105 using millimeter wave radar.

At block 1220, sensor data is analyzed. The data analysis may includeusing a computing module 130 to process sensor data captured by theremote sensor 120, as described elsewhere herein. In some embodiments,the data analysis may be performed by other components, such as imageprocessor 330, as described elsewhere herein.

The data analysis of block 1220 may include analyzing sensor data todetermine whether an obstacle is present in the area around the mobilityapparatus. For example, the data analysis may include determiningwhether an obstacle is present in one or more sectors sensed by theremote sensor 120 in front of the mobility apparatus 105. The dataanalysis of block 1220 may include applying a decision tree structure orother previously-trained structure.

At block 1230, a determination is made as to whether there is anobstacle. For example, a determination may be made as to whether anobstacle is present in one or more sectors sensed by the remote sensor120 in front of the mobility apparatus 105. In some embodiments, block1230 may include using a result of the analysis performed at block 1220.

At block 1230, if it is determined that no obstacle is present, then theprocess continues at block 1210.

At block 1230, if it is determined that an obstacle is present, then theprocess continues at block 1240.

At block 1240, a safe velocity is calculated. The calculation may bemade by computing module 130 in some embodiments. The calculation may bemade by drive controller 110 in some embodiments.

The calculation at block 1240 may include calculating a velocity for themobility apparatus that will avoid a collision with the obstacle. Forexample, the calculation may include determining whether increasingvelocity or decreasing velocity is more likely to avoid a collision. Thedetermination may be made based on determining a trajectory for themobility apparatus. As another example, the calculation may includedetermining a maximum velocity that will reduce the force imparted onthe user of the mobility apparatus below a predetermined thresholdshould a collision occur (e.g., determine the highest velocity that themobility apparatus can still be traveling while reducing collisionimpact to at most 1 G). The calculation at block 1240 may be performedin different ways in various embodiments. In some embodiments, thecalculation may be performed based on a an artificial intelligencestructure.

At block 1250, a current velocity is determined. The calculation may bemade by computing module 130 in some embodiments. The calculation may bemade by drive controller 110 in some embodiments.

The calculation at block 1250 may be performed in a variety of ways. Forexample, the drive controller 110 may calculate a current velocity ofthe mobility apparatus based on a measured number of revolutions perminute of the rear wheel of the mobility apparatus. As another example,the computing module 130 may calculate a current velocity of themobility apparatus based on a global positioning system receiver presenton the mobility apparatus. The calculation may be performed in otherways in various embodiments.

At block 1260, acceleration of a mobility apparatus is controlled. Theacceleration of the mobility apparatus 105 may be controlled using thedrive controller 110, as described elsewhere herein. The acceleration ofthe mobility apparatus may be performed based on the calculationperformed at block 1240 and/or 1250. The acceleration of the mobilityapparatus may be controlled based on a control instruction generatedbased on the calculation performed at block 1240 and/or 1250. Theacceleration of the mobility apparatus may be controlled based on adifference value between the safe velocity calculated at block 1240 andthe current velocity determined at block 1250. In some embodiments,controlling the acceleration of the mobility apparatus may includeapplying brakes of the mobility apparatus, releasing brakes of themobility apparatus, engaging throttle of the mobility apparatus, and/ordisengaging a throttle of the mobility apparatus.

In various embodiments, the process 1200 may include more or fewerblocks than those just escribed. For example, the process 1200 mayinclude the remote sensor 120 transmitting sensor data to the computingdevice 130. As another example, the process 1200 may include thecomputing device filtering the sensor data received from the remotesensor 120. In such embodiments, the computing module 130 may performthe data analysis of block 1220 using the filtered sensor data. Asanother example, the process 1200 may include the computing devicefiltering an output of the processing at block 1220.

FIG. 13A is a schematic diagram of remote sensing 1350 for a mobilityapparatus according to some embodiments of the present disclosure.Remote sensing 1350 may be provided as described elsewhere herein. Themobility apparatus may be moving according to a trajectory 1305.

Remote sensing 1350 may include detecting the presence of obstacle 1310in the area in front of the mobility apparatus 105. The obstacle 1310may be moving according to a trajectory 1315.

Remote sensing 1350 may include detecting the presence of obstacle 1320in the area in front of the mobility apparatus 105. The obstacle 1320may be moving according to a trajectory 1305.

FIG. 13B is a schematic diagram of remote sensing 1350 for a mobilityapparatus according to some embodiments of the present disclosure.Remote sensing 1350 may be provided as described elsewhere herein.Remote sensing 1350 may include categorization of sectors as describedelsewhere herein.

Remote sensing 1350 may include detecting the presence of obstacle 1330in the area in front of the mobility apparatus 105. The obstacle 1330may be moving according to a trajectory 1335. Remote sensing 1350 mayinclude detecting the trajectory 1335 with respect to the varioussectors in the remote sensing 1350. Remote sensing 1350 may includedetecting the trajectory 1335 with respect to the categories or classesof sectors in remote sensing 1350.

FIG. 14 is a flowchart for a process 1400 of controlling a mobilityapparatus according to some embodiments of the present disclosure. Theprocess 1400 may be performed using the mobility system 100 in someembodiments.

At block 1410, remote sensing is performed in the area around a mobilityapparatus. The remote sensing may include using a remote sensor 120, asdescribed elsewhere herein. The remote sensing may include sensing anarea in front of the mobility apparatus 105 using millimeter wave radar.

At block 1420, sensor data is analyzed. The data analysis may includeusing a computing module 130 to process sensor data captured by theremote sensor 120, as described elsewhere herein. In some embodiments,the data analysis may be performed by other components, such as imageprocessor 330, as described elsewhere herein.

The data analysis of block 1420 may include analyzing sensor data todetermine whether an obstacle is present in the area around the mobilityapparatus. For example, the data analysis may include determiningwhether an obstacle is present in one or more sectors sensed by theremote sensor 120 in front of the mobility apparatus 105. The dataanalysis of block 1420 may include applying a decision tree structure orother previously-trained structure.

At block 1430, a determination is made as to whether there is anobstacle. For example, a determination may be made as to whether anobstacle is present in one or more sectors sensed by the remote sensor120 in front of the mobility apparatus 105. In some embodiments, block1430 may include using a result of the analysis performed at block 1420.

At block 1430, if it is determined that no obstacle is present, then theprocess continues at block 1410.

At block 1430, if it is determined that an obstacle is present, then theprocess continues at block 1440.

At block 1440, a determination is made as to whether the mobilityapparatus 105 will collide with the obstacle. The determination may bemade by computing module 130 in some embodiments.

The determination at block 1440 may be made in various ways. Forexample, the determination may be made by calculating a trajectory ofthe obstacle (e.g., trajectory 1315 (with reference to FIG. 13A),calculating a trajectory of the mobility apparatus 105 (e.g., trajectory1305 (with reference to FIG. 13A)), and comparing the trajectories todetermine if they will intersect. As another example, the determinationmay be made by determining if the whether the obstacle will come withina predefined minimum radius of the mobility apparatus 105. Thedetermination may be made in other ways in various embodiments.

At block 1440, if it is determined that the mobility apparatus will notcollide with the obstacle, then the process continues at block 1410.

At block 1440, if it is determined that the mobility apparatus willcollide with the obstacle, then the process continues at block 1450.

At block 1450, acceleration of a mobility apparatus is controlled. Theacceleration of the mobility apparatus 105 may be controlled using thedrive controller 110, as described elsewhere herein. The acceleration ofthe mobility apparatus may be performed based on the determinationperformed at block 1440. The acceleration of the mobility apparatus maybe controlled based on a control instruction generated based on thedetermination performed at block 1440. In some embodiments, controllingthe acceleration of the mobility apparatus may include applying brakesof the mobility apparatus, releasing brakes of the mobility apparatus,engaging throttle of the mobility apparatus, and/or disengaging athrottle of the mobility apparatus.

In various embodiments, the process 1400 may include more or fewerblocks than those just escribed. For example, the process 1400 mayinclude the remote sensor 120 transmitting sensor data to the computingdevice 130. As another example, the process 1400 may include thecomputing device filtering the sensor data received from the remotesensor 120. In such embodiments, the computing module 130 may performthe data analysis of block 1420 using the filtered sensor data. Asanother example, the process 1400 may include the computing devicefiltering an output of the processing at block 1420.

FIG. 15 is a flowchart for a process 1500 of controlling a mobilityapparatus according to some embodiments of the present disclosure. Theprocess 1500 may be performed using the mobility system 100 in someembodiments.

At block 1510, remote sensing is performed in the area around a mobilityapparatus. The remote sensing may include using a remote sensor 120, asdescribed elsewhere herein. The remote sensing may include sensing anarea in front of the mobility apparatus 105 using millimeter wave radar.

At block 1520, sensor data is analyzed. The data analysis may includeusing a computing module 130 to process sensor data captured by theremote sensor 120, as described elsewhere herein. In some embodiments,the data analysis may be performed by other components, such as imageprocessor 330, as described elsewhere herein.

The data analysis of block 1520 may include analyzing sensor data todetermine whether an obstacle is present in the area around the mobilityapparatus. For example, the data analysis may include determiningwhether an obstacle is present in one or more sectors sensed by theremote sensor 120 in front of the mobility apparatus 105. The dataanalysis of block 1520 may include applying a decision tree structure orother previously-trained structure.

At block 1530, a determination is made as to whether there is anobstacle. For example, a determination may be made as to whether anobstacle is present in one or more sectors sensed by the remote sensor120 in front of the mobility apparatus 105. In some embodiments, block1530 may include using a result of the analysis performed at block 1520.

At block 1530, if it is determined that no obstacle is present, then theprocess continues at block 1510.

At block 1530, if it is determined that an obstacle is present, then theprocess continues at block 1540.

At block 1540, a determination is made as to whether the obstacle willenter a high risk sector. The determination may be made by computingmodule 130 in some embodiments.

The determination at block 1540 may be made in various ways. Forexample, the determination may be made by calculating a trajectory ofthe obstacle (e.g., trajectory 1335 (with reference to FIG. 13B), andcomparing the trajectory to the present location of sectors categorizedas high risk. As another example, the determination may be made bycalculating a trajectory of the obstacle (e.g., trajectory 1335 (withreference to FIG. 13B), and comparing the trajectory to expected futurelocations of sectors categorized as high risk. The expected futurelocations of the sectors may be determined based on determining atrajectory of the mobility apparatus 105 (e.g., trajectory 1305 (withreference to FIG. 13A)).

At block 1540, if it is determined that the obstacle will not enter ahigh risk sector, then the process continues at block 1510.

At block 1540, if it is determined that the obstacle will enter a highrisk sector, then the process continues at block 1550.

At block 1550, acceleration of a mobility apparatus is controlled. Theacceleration of the mobility apparatus 105 may be controlled using thedrive controller 110, as described elsewhere herein. The acceleration ofthe mobility apparatus may be performed based on the determinationperformed at block 1540. The acceleration of the mobility apparatus maybe controlled based on a control instruction generated based on thedetermination performed at block 1540. In some embodiments, controllingthe acceleration of the mobility apparatus may include applying brakesof the mobility apparatus, releasing brakes of the mobility apparatus,engaging throttle of the mobility apparatus, and/or disengaging athrottle of the mobility apparatus.

In various embodiments, the process 1500 may include more or fewerblocks than those just escribed. For example, the process 1500 mayinclude the remote sensor 120 transmitting sensor data to the computingdevice 130. As another example, the process 1500 may include thecomputing device filtering the sensor data received from the remotesensor 120. In such embodiments, the computing module 130 may performthe data analysis of block 1520 using the filtered sensor data. Asanother example, the process 1500 may include the computing devicefiltering an output of the processing at block 1520.

The various processes and remote sensing disclosed herein may becombined consistent with the present disclosure.

In some embodiments, process 1100 (with reference to FIG. 11) may bemodified to include the trajectory of the obstacle as part of theprocess. For example, process 1100 may be modified to include a block ofdetermining a trajectory of the detected obstacle. This block may beplaced between blocks 1130 and 1140. Then at block 1140, the process1100 may include using the determined trajectory of the detectedobstacle to calculate the quantity of acceleration control to be appliedto the mobility apparatus 105. For example, the block 1140 may includecalculating a sufficient reduction in acceleration so that the obstaclewill pass out of the trajectory of the mobility apparatus 105 before thetwo trajectories intersect (e.g., slow down the mobility apparatus 105so that obstacle 1310 fully passes across the front of mobilityapparatus 105 before the two trajectories cross).

In some embodiments, process 1400 (with reference to FIG. 14) may bemodified to include the type of the obstacle, such as disclosed withrespect to block 1040 (with reference to FIG. 10). For example, theprocess 1400 may be modified so that the determination of 1440 is madeadditionally based on a determination of the type of the obstacle. Forexample, the determination may calculation eh trajectory of the obstacleby first determining the type of obstacle (e.g., trajectory of bicyclelikely to remain straight, while trajectory of pedestrian more likely tochange path of travel).

From the foregoing, it will be appreciated that specific embodiments ofthe invention have been described herein for purposes of illustration,but that various modifications may be made without deviating from thescope of the invention. Accordingly, the invention is not limited exceptas by the appended claims.

I/We claim:
 1. A system, comprising: a mobility apparatus; a remotesensor configured to generate sensor data about a vicinity of themobility apparatus; a computing module configured to determine, based onthe sensor data, whether an obstacle is present in the vicinity of themobility apparatus; and a drive controller configured to alter theacceleration of the mobility apparatus responsive to the determinationby the computing module as to whether an obstacle is present in thevicinity of the mobility apparatus.
 2. The system of claim 1, whereinthe mobility apparatus is an electric scooter.
 3. The system of claim 3,wherein the remote sensor is a radar apparatus that emitselectromagnetic waves in the millimeter range.
 4. The system of claim 2,wherein the drive controller alters the acceleration of the mobilityapparatus by applying a braking mechanism of the mobility apparatus. 5.The system of claim 4, wherein the braking mechanism is a regenerativebraking mechanism.
 6. The system of claim 5, wherein the computingmodule is further configured to determine a trajectory of the obstacle.7. The system of claim 6, wherein the computing module is configured todetermine whether an obstacle is present in the vicinity of the mobilityapparatus by determining whether an object is present in one or moresectors sensed by the remote sensor.
 8. The system of claim 7, whereinthe computing module is further configured to determine in which sectorsensed by the remote sensor the object is present.
 9. The system ofclaim 8, wherein the computing module is further configured to determinewhether the object is in a high risk sector for the mobility apparatus.10. The system of claim 9, wherein the computing module is furtherconfigured to determine a type of obstacle that the object is.
 11. Amethod comprising: generating sensor data about a vicinity of a mobilityapparatus using a remote sensor; determining, based on the sensor data,whether an obstacle is present in the vicinity of the mobilityapparatus; and altering, using a drive controller, the acceleration ofthe mobility apparatus responsive to the determining of whether anobstacle is present in the vicinity of the mobility apparatus.
 12. Themethod of claim 11, wherein the mobility apparatus is an electricscooter.
 13. The method of claim 13, wherein the remote sensor is aradar apparatus that emits electromagnetic waves in the millimeterrange.
 14. The method of claim 12, wherein the drive controller performsthe altering of the acceleration of the mobility apparatus at least inpart by applying a braking mechanism of the mobility apparatus.
 15. Themethod of claim 14, wherein the braking mechanism is a regenerativebraking mechanism.
 16. The method of claim 15, further comprising:determining a trajectory of the obstacle.
 17. The method of claim 16,wherein the determining of whether an obstacle is present in thevicinity of the mobility apparatus is performed at least in part bydetermining whether an object is present in one or more sectors sensedby the remote sensor.
 18. The method of claim 17, further comprising:determining in which sector sensed by the remote sensor the object ispresent.
 19. The method of claim 18, further comprising: determiningwhether the object is in a high risk sector for the mobility apparatus.20. The method of claim 19, further comprising: determining a type ofobstacle that the object is.